人脸生成(Face Generation)

在该项目中,你将使用生成式对抗网络(Generative Adversarial Nets)来生成新的人脸图像。

获取数据

该项目将使用以下数据集:

  • MNIST
  • CelebA

由于 CelebA 数据集比较复杂,而且这是你第一次使用 GANs。我们想让你先在 MNIST 数据集上测试你的 GANs 模型,以让你更快的评估所建立模型的性能。

如果你在使用 FloydHub, 请将 data_dir 设置为 "/input" 并使用 FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

探索数据(Explore the Data)

MNIST

MNIST 是一个手写数字的图像数据集。你可以更改 show_n_images 探索此数据集。

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x1c67f465c88>

CelebA

CelebFaces Attributes Dataset (CelebA) 是一个包含 20 多万张名人图片及相关图片说明的数据集。你将用此数据集生成人脸,不会用不到相关说明。你可以更改 show_n_images 探索此数据集。

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x1c60e015278>

预处理数据(Preprocess the Data)

由于该项目的重点是建立 GANs 模型,我们将为你预处理数据。

经过数据预处理,MNIST 和 CelebA 数据集的值在 28×28 维度图像的 [-0.5, 0.5] 范围内。CelebA 数据集中的图像裁剪了非脸部的图像部分,然后调整到 28x28 维度。

MNIST 数据集中的图像是单通道的黑白图像,CelebA 数据集中的图像是 三通道的 RGB 彩色图像

建立神经网络(Build the Neural Network)

你将通过部署以下函数来建立 GANs 的主要组成部分:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

检查 TensorFlow 版本并获取 GPU 型号

检查你是否使用正确的 TensorFlow 版本,并获取 GPU 型号

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.1
Default GPU Device: /gpu:0

输入(Input)

部署 model_inputs 函数以创建用于神经网络的 占位符 (TF Placeholders)。请创建以下占位符:

  • 输入图像占位符: 使用 image_widthimage_heightimage_channels 设置为 rank 4。
  • 输入 Z 占位符: 设置为 rank 2,并命名为 z_dim
  • 学习速率占位符: 设置为 rank 0。

返回占位符元组的形状为 (tensor of real input images, tensor of z data, learning rate)。

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    input_real=tf.placeholder(tf.float32,[None,image_width, image_height, image_channels],name='input_real')
    input_z=tf.placeholder(tf.float32,[None,z_dim],name='input_z')
    learn_rate=tf.placeholder(tf.float32,[],'learn_rate')
    return input_real,input_z,learn_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

辨别器(Discriminator)

部署 discriminator 函数创建辨别器神经网络以辨别 images。该函数应能够重复使用神经网络中的各种变量。 在 tf.variable_scope 中使用 "discriminator" 的变量空间名来重复使用该函数中的变量。

该函数应返回形如 (tensor output of the discriminator, tensor logits of the discriminator) 的元组。

In [6]:
def discriminator(images, reuse=False,alpha=0.1):
    """
    Create the discriminator network
    :param image: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    with tf.variable_scope('discriminator',reuse=reuse):
        x1=tf.layers.conv2d(images,64,5,strides=2,padding='same',kernel_initializer= tf.contrib.layers.xavier_initializer())
        relu1 = tf.maximum(alpha * x1, x1)
        #relu1=tf.nn.dropout(relu1,keep_prob=0.8)
        
        x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same',kernel_initializer= tf.contrib.layers.xavier_initializer())
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = tf.maximum(alpha * bn2, bn2)
        #relu2= tf.nn.dropout(relu2,keep_prob=0.8)

        
        x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='same',kernel_initializer= tf.contrib.layers.xavier_initializer())
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = tf.maximum(alpha * bn3, bn3)
        relu3= tf.nn.dropout(relu3,keep_prob=0.8)
        
        flat = tf.reshape(relu3, (-1, 4*4*256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
    return out,logits

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

生成器(Generator)

部署 generator 函数以使用 z 生成图像。该函数应能够重复使用神经网络中的各种变量。 在 tf.variable_scope 中使用 "generator" 的变量空间名来重复使用该函数中的变量。

该函数应返回所生成的 28 x 28 x out_channel_dim 维度图像。

In [7]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    
    with tf.variable_scope('generator', reuse=not is_train):
        alpha = 0.01
    
        h1 = tf.layers.dense(z, 2*2*512)
        h1 = tf.reshape(h1, (-1, 2, 2, 512))
        h1 = tf.layers.batch_normalization(h1, training=is_train)
        h1 = tf.maximum(alpha * h1, h1)
    
        h2 = tf.layers.conv2d_transpose(h1, 256, 5, 2, 'valid',kernel_initializer= tf.contrib.layers.xavier_initializer())
        h2 = tf.layers.batch_normalization(h2, training=is_train)
        h2 = tf.maximum(alpha * h2, h2)
    
        h3 = tf.layers.conv2d_transpose(h2, 128, 5, 2, 'same',kernel_initializer= tf.contrib.layers.xavier_initializer())
        h3 = tf.layers.batch_normalization(h3, training=is_train)
        h3 = tf.maximum(alpha * h3, h3)
    
        logits = tf.layers.conv2d_transpose(h3, out_channel_dim, 5, 2, 'same',kernel_initializer= tf.contrib.layers.xavier_initializer())
        out = tf.tanh(logits)
    
        return out



"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

损失函数(Loss)

部署 model_loss 函数训练并计算 GANs 的损失。该函数应返回形如 (discriminator loss, generator loss) 的元组。

使用你已实现的函数:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    g_model=generator(input_z, out_channel_dim, is_train=True)
    d_model_real,d_real_logits=discriminator(input_real)
    d_model_fake,d_fake_logits=discriminator(g_model,reuse=True)
    
    smooth=0.1
    d_loss_real=tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_real_logits,labels=tf.ones_like(d_real_logits) * (1 - smooth)))
    d_loss_fake=tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_fake_logits,labels=tf.zeros_like(d_model_fake)))
    g_loss=tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_fake_logits,labels=tf.ones_like(d_model_fake)))
    
    d_loss=d_loss_real+d_loss_fake
    
    return d_loss,g_loss

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

优化(Optimization)

部署 model_opt 函数实现对 GANs 的优化。使用 tf.trainable_variables 获取可训练的所有变量。通过变量空间名 discriminatorgenerator 来过滤变量。该函数应返回形如 (discriminator training operation, generator training operation) 的元组。

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
    
    with tf.control_dependencies(update_ops):
        
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

        return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

训练神经网络(Neural Network Training)

输出显示

使用该函数可以显示生成器 (Generator) 在训练过程中的当前输出,这会帮你评估 GANs 模型的训练程度。

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

训练

部署 train 函数以建立并训练 GANs 模型。记得使用以下你已完成的函数:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

使用 show_generator_output 函数显示 generator 在训练过程中的输出。

注意:在每个批次 (batch) 中运行 show_generator_output 函数会显著增加训练时间与该 notebook 的体积。推荐每 100 批次输出一次 generator 的输出。

In [11]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    input_real, input_z, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)

    d_loss, g_loss = model_loss(input_real, input_z, data_shape[3])

    d_opt, g_opt = model_opt(d_loss, g_loss, lr, beta1)
    
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            steps = 0
            for batch_images in get_batches(batch_size):
                steps +=1
                batch_images = batch_images * 2
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                # Run optimizers
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
                _ = sess.run(g_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
                
                if steps % 10 == 0:
                    train_loss_d = d_loss.eval({input_real: batch_images, input_z: batch_z})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{}...".format(epoch_i+1, epochs),
                          "Batch {}...".format(steps),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))

                if steps % 100 == 0:
                    show_generator_output(sess, show_n_images, input_z, data_shape[3], data_image_mode)
                

MNIST

在 MNIST 上测试你的 GANs 模型。经过 2 次迭代,GANs 应该能够生成类似手写数字的图像。确保生成器 (generator) 低于辨别器 (discriminator) 的损失,或接近 0。

In [13]:
batch_size = 32
z_dim = 128
learning_rate = 0.0002
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Batch 10... Discriminator Loss: 0.8576... Generator Loss: 1.2394
Epoch 1/2... Batch 20... Discriminator Loss: 0.4599... Generator Loss: 2.4291
Epoch 1/2... Batch 30... Discriminator Loss: 2.5728... Generator Loss: 0.1242
Epoch 1/2... Batch 40... Discriminator Loss: 0.6066... Generator Loss: 1.8103
Epoch 1/2... Batch 50... Discriminator Loss: 0.5147... Generator Loss: 4.3227
Epoch 1/2... Batch 60... Discriminator Loss: 0.4111... Generator Loss: 3.7045
Epoch 1/2... Batch 70... Discriminator Loss: 0.7538... Generator Loss: 1.2395
Epoch 1/2... Batch 80... Discriminator Loss: 0.7241... Generator Loss: 7.0373
Epoch 1/2... Batch 90... Discriminator Loss: 0.4158... Generator Loss: 3.6974
Epoch 1/2... Batch 100... Discriminator Loss: 0.8049... Generator Loss: 1.1951
Epoch 1/2... Batch 110... Discriminator Loss: 0.4689... Generator Loss: 3.0312
Epoch 1/2... Batch 120... Discriminator Loss: 0.4464... Generator Loss: 3.4445
Epoch 1/2... Batch 130... Discriminator Loss: 0.4856... Generator Loss: 2.5549
Epoch 1/2... Batch 140... Discriminator Loss: 0.4878... Generator Loss: 4.0570
Epoch 1/2... Batch 150... Discriminator Loss: 0.4437... Generator Loss: 2.8395
Epoch 1/2... Batch 160... Discriminator Loss: 0.6906... Generator Loss: 1.8459
Epoch 1/2... Batch 170... Discriminator Loss: 0.4545... Generator Loss: 3.6153
Epoch 1/2... Batch 180... Discriminator Loss: 0.4599... Generator Loss: 3.0296
Epoch 1/2... Batch 190... Discriminator Loss: 0.4055... Generator Loss: 3.9982
Epoch 1/2... Batch 200... Discriminator Loss: 0.4209... Generator Loss: 5.6488
Epoch 1/2... Batch 210... Discriminator Loss: 0.4259... Generator Loss: 3.3996
Epoch 1/2... Batch 220... Discriminator Loss: 0.4183... Generator Loss: 3.4513
Epoch 1/2... Batch 230... Discriminator Loss: 0.4103... Generator Loss: 5.0700
Epoch 1/2... Batch 240... Discriminator Loss: 0.4037... Generator Loss: 3.9823
Epoch 1/2... Batch 250... Discriminator Loss: 0.5740... Generator Loss: 2.0881
Epoch 1/2... Batch 260... Discriminator Loss: 0.4689... Generator Loss: 6.4767
Epoch 1/2... Batch 270... Discriminator Loss: 0.4899... Generator Loss: 2.5621
Epoch 1/2... Batch 280... Discriminator Loss: 0.4626... Generator Loss: 3.0954
Epoch 1/2... Batch 290... Discriminator Loss: 0.4242... Generator Loss: 3.5065
Epoch 1/2... Batch 300... Discriminator Loss: 0.4450... Generator Loss: 3.5193
Epoch 1/2... Batch 310... Discriminator Loss: 0.3851... Generator Loss: 4.2154
Epoch 1/2... Batch 320... Discriminator Loss: 0.4195... Generator Loss: 3.5378
Epoch 1/2... Batch 330... Discriminator Loss: 0.4122... Generator Loss: 4.2416
Epoch 1/2... Batch 340... Discriminator Loss: 0.4033... Generator Loss: 4.7998
Epoch 1/2... Batch 350... Discriminator Loss: 0.4153... Generator Loss: 3.6666
Epoch 1/2... Batch 360... Discriminator Loss: 0.4094... Generator Loss: 3.5469
Epoch 1/2... Batch 370... Discriminator Loss: 0.4350... Generator Loss: 3.3561
Epoch 1/2... Batch 380... Discriminator Loss: 0.3920... Generator Loss: 3.3964
Epoch 1/2... Batch 390... Discriminator Loss: 0.4287... Generator Loss: 4.4200
Epoch 1/2... Batch 400... Discriminator Loss: 0.4304... Generator Loss: 3.7388
Epoch 1/2... Batch 410... Discriminator Loss: 0.5202... Generator Loss: 2.6026
Epoch 1/2... Batch 420... Discriminator Loss: 0.4165... Generator Loss: 4.0918
Epoch 1/2... Batch 430... Discriminator Loss: 0.4254... Generator Loss: 3.5968
Epoch 1/2... Batch 440... Discriminator Loss: 0.4212... Generator Loss: 3.8853
Epoch 1/2... Batch 450... Discriminator Loss: 0.3912... Generator Loss: 4.0111
Epoch 1/2... Batch 460... Discriminator Loss: 0.5692... Generator Loss: 2.1578
Epoch 1/2... Batch 470... Discriminator Loss: 0.4217... Generator Loss: 3.9862
Epoch 1/2... Batch 480... Discriminator Loss: 0.4265... Generator Loss: 3.2029
Epoch 1/2... Batch 490... Discriminator Loss: 0.4017... Generator Loss: 4.6775
Epoch 1/2... Batch 500... Discriminator Loss: 0.3613... Generator Loss: 3.8597
Epoch 1/2... Batch 510... Discriminator Loss: 0.4431... Generator Loss: 4.4495
Epoch 1/2... Batch 520... Discriminator Loss: 0.4116... Generator Loss: 4.6207
Epoch 1/2... Batch 530... Discriminator Loss: 0.4238... Generator Loss: 4.2848
Epoch 1/2... Batch 540... Discriminator Loss: 0.4374... Generator Loss: 3.8228
Epoch 1/2... Batch 550... Discriminator Loss: 0.4425... Generator Loss: 3.3396
Epoch 1/2... Batch 560... Discriminator Loss: 0.4864... Generator Loss: 3.4079
Epoch 1/2... Batch 570... Discriminator Loss: 0.4415... Generator Loss: 3.8742
Epoch 1/2... Batch 580... Discriminator Loss: 0.4243... Generator Loss: 5.1201
Epoch 1/2... Batch 590... Discriminator Loss: 0.4586... Generator Loss: 3.0156
Epoch 1/2... Batch 600... Discriminator Loss: 0.3950... Generator Loss: 4.1587
Epoch 1/2... Batch 610... Discriminator Loss: 0.4055... Generator Loss: 3.9555
Epoch 1/2... Batch 620... Discriminator Loss: 0.3997... Generator Loss: 3.8675
Epoch 1/2... Batch 630... Discriminator Loss: 0.4987... Generator Loss: 2.4254
Epoch 1/2... Batch 640... Discriminator Loss: 0.4129... Generator Loss: 4.5010
Epoch 1/2... Batch 650... Discriminator Loss: 0.4237... Generator Loss: 6.9321
Epoch 1/2... Batch 660... Discriminator Loss: 0.5514... Generator Loss: 2.3181
Epoch 1/2... Batch 670... Discriminator Loss: 0.5862... Generator Loss: 2.3498
Epoch 1/2... Batch 680... Discriminator Loss: 0.4270... Generator Loss: 3.9300
Epoch 1/2... Batch 690... Discriminator Loss: 0.4862... Generator Loss: 5.6653
Epoch 1/2... Batch 700... Discriminator Loss: 0.4203... Generator Loss: 3.8231
Epoch 1/2... Batch 710... Discriminator Loss: 0.4903... Generator Loss: 2.4607
Epoch 1/2... Batch 720... Discriminator Loss: 0.5481... Generator Loss: 2.3645
Epoch 1/2... Batch 730... Discriminator Loss: 0.4244... Generator Loss: 4.5998
Epoch 1/2... Batch 740... Discriminator Loss: 0.4462... Generator Loss: 3.6461
Epoch 1/2... Batch 750... Discriminator Loss: 0.4583... Generator Loss: 2.8285
Epoch 1/2... Batch 760... Discriminator Loss: 0.4545... Generator Loss: 3.7672
Epoch 1/2... Batch 770... Discriminator Loss: 0.4600... Generator Loss: 3.3338
Epoch 1/2... Batch 780... Discriminator Loss: 0.5730... Generator Loss: 2.4440
Epoch 1/2... Batch 790... Discriminator Loss: 0.4520... Generator Loss: 3.2829
Epoch 1/2... Batch 800... Discriminator Loss: 1.4694... Generator Loss: 0.6864
Epoch 1/2... Batch 810... Discriminator Loss: 0.6055... Generator Loss: 1.9856
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Epoch 2/2... Batch 1580... Discriminator Loss: 1.0088... Generator Loss: 1.1289
Epoch 2/2... Batch 1590... Discriminator Loss: 0.9806... Generator Loss: 2.5540
Epoch 2/2... Batch 1600... Discriminator Loss: 0.9727... Generator Loss: 1.1601
Epoch 2/2... Batch 1610... Discriminator Loss: 1.0271... Generator Loss: 1.0680
Epoch 2/2... Batch 1620... Discriminator Loss: 0.8810... Generator Loss: 1.3475
Epoch 2/2... Batch 1630... Discriminator Loss: 0.8201... Generator Loss: 1.6401
Epoch 2/2... Batch 1640... Discriminator Loss: 0.7520... Generator Loss: 1.5360
Epoch 2/2... Batch 1650... Discriminator Loss: 0.6275... Generator Loss: 1.9112
Epoch 2/2... Batch 1660... Discriminator Loss: 0.7507... Generator Loss: 1.3094
Epoch 2/2... Batch 1670... Discriminator Loss: 1.1888... Generator Loss: 0.7619
Epoch 2/2... Batch 1680... Discriminator Loss: 0.6409... Generator Loss: 2.0935
Epoch 2/2... Batch 1690... Discriminator Loss: 0.9190... Generator Loss: 0.8875
Epoch 2/2... Batch 1700... Discriminator Loss: 0.8269... Generator Loss: 1.2518
Epoch 2/2... Batch 1710... Discriminator Loss: 1.3486... Generator Loss: 0.8026
Epoch 2/2... Batch 1720... Discriminator Loss: 1.0955... Generator Loss: 1.4153
Epoch 2/2... Batch 1730... Discriminator Loss: 0.6859... Generator Loss: 1.6580
Epoch 2/2... Batch 1740... Discriminator Loss: 0.7868... Generator Loss: 1.6641
Epoch 2/2... Batch 1750... Discriminator Loss: 0.6357... Generator Loss: 2.0032
Epoch 2/2... Batch 1760... Discriminator Loss: 0.7302... Generator Loss: 1.3410
Epoch 2/2... Batch 1770... Discriminator Loss: 0.8790... Generator Loss: 1.2924
Epoch 2/2... Batch 1780... Discriminator Loss: 0.7127... Generator Loss: 1.6253
Epoch 2/2... Batch 1790... Discriminator Loss: 0.8078... Generator Loss: 1.7116
Epoch 2/2... Batch 1800... Discriminator Loss: 0.6987... Generator Loss: 1.5257
Epoch 2/2... Batch 1810... Discriminator Loss: 0.8006... Generator Loss: 1.7880
Epoch 2/2... Batch 1820... Discriminator Loss: 0.7884... Generator Loss: 1.5224
Epoch 2/2... Batch 1830... Discriminator Loss: 0.8963... Generator Loss: 1.2946
Epoch 2/2... Batch 1840... Discriminator Loss: 0.7492... Generator Loss: 1.5460
Epoch 2/2... Batch 1850... Discriminator Loss: 0.7817... Generator Loss: 1.2037
Epoch 2/2... Batch 1860... Discriminator Loss: 1.2429... Generator Loss: 0.6506
Epoch 2/2... Batch 1870... Discriminator Loss: 0.6745... Generator Loss: 1.8051

CelebA

在 CelebA 上运行你的 GANs 模型。在一般的GPU上运行每次迭代大约需要 20 分钟。你可以运行整个迭代,或者当 GANs 开始产生真实人脸图像时停止它。

In [12]:
batch_size = 32
z_dim = 128
learning_rate = 0.0002
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Batch 10... Discriminator Loss: 1.1526... Generator Loss: 1.0004
Epoch 1/1... Batch 20... Discriminator Loss: 0.9597... Generator Loss: 1.4469
Epoch 1/1... Batch 30... Discriminator Loss: 0.6848... Generator Loss: 1.6245
Epoch 1/1... Batch 40... Discriminator Loss: 0.4225... Generator Loss: 4.5964
Epoch 1/1... Batch 50... Discriminator Loss: 0.6896... Generator Loss: 1.5810
Epoch 1/1... Batch 60... Discriminator Loss: 0.4560... Generator Loss: 6.6155
Epoch 1/1... Batch 70... Discriminator Loss: 0.4855... Generator Loss: 5.2753
Epoch 1/1... Batch 80... Discriminator Loss: 0.9493... Generator Loss: 12.1851
Epoch 1/1... Batch 90... Discriminator Loss: 1.3155... Generator Loss: 10.1974
Epoch 1/1... Batch 100... Discriminator Loss: 0.6290... Generator Loss: 3.1237
Epoch 1/1... Batch 110... Discriminator Loss: 0.5461... Generator Loss: 4.9995
Epoch 1/1... Batch 120... Discriminator Loss: 0.4408... Generator Loss: 3.5353
Epoch 1/1... Batch 130... Discriminator Loss: 0.4745... Generator Loss: 2.7503
Epoch 1/1... Batch 140... Discriminator Loss: 0.4979... Generator Loss: 2.5212
Epoch 1/1... Batch 150... Discriminator Loss: 0.4831... Generator Loss: 3.5012
Epoch 1/1... Batch 160... Discriminator Loss: 0.4100... Generator Loss: 3.6750
Epoch 1/1... Batch 170... Discriminator Loss: 0.5712... Generator Loss: 2.3501
Epoch 1/1... Batch 180... Discriminator Loss: 0.5200... Generator Loss: 2.5742
Epoch 1/1... Batch 190... Discriminator Loss: 0.4858... Generator Loss: 2.6696
Epoch 1/1... Batch 200... Discriminator Loss: 0.4276... Generator Loss: 3.5730
Epoch 1/1... Batch 210... Discriminator Loss: 0.3901... Generator Loss: 4.4024
Epoch 1/1... Batch 220... Discriminator Loss: 0.4145... Generator Loss: 3.7769
Epoch 1/1... Batch 230... Discriminator Loss: 0.5913... Generator Loss: 2.2760
Epoch 1/1... Batch 240... Discriminator Loss: 0.4316... Generator Loss: 2.8275
Epoch 1/1... Batch 250... Discriminator Loss: 0.3901... Generator Loss: 3.9956
Epoch 1/1... Batch 260... Discriminator Loss: 0.4860... Generator Loss: 2.3743
Epoch 1/1... Batch 270... Discriminator Loss: 0.4262... Generator Loss: 3.3637
Epoch 1/1... Batch 280... Discriminator Loss: 0.4683... Generator Loss: 2.5070
Epoch 1/1... Batch 290... Discriminator Loss: 0.7003... Generator Loss: 1.4855
Epoch 1/1... Batch 300... Discriminator Loss: 0.7343... Generator Loss: 1.5854
Epoch 1/1... Batch 310... Discriminator Loss: 0.9269... Generator Loss: 0.8718
Epoch 1/1... Batch 320... Discriminator Loss: 0.6369... Generator Loss: 2.5311
Epoch 1/1... Batch 330... Discriminator Loss: 0.5826... Generator Loss: 2.0272
Epoch 1/1... Batch 340... Discriminator Loss: 0.6400... Generator Loss: 2.0120
Epoch 1/1... Batch 350... Discriminator Loss: 0.7545... Generator Loss: 1.7533
Epoch 1/1... Batch 360... Discriminator Loss: 0.6958... Generator Loss: 4.7545
Epoch 1/1... Batch 370... Discriminator Loss: 0.7449... Generator Loss: 1.4024
Epoch 1/1... Batch 380... Discriminator Loss: 0.7446... Generator Loss: 1.7935
Epoch 1/1... Batch 390... Discriminator Loss: 0.8522... Generator Loss: 1.6627
Epoch 1/1... Batch 400... Discriminator Loss: 0.5521... Generator Loss: 2.3472
Epoch 1/1... Batch 410... Discriminator Loss: 0.7131... Generator Loss: 1.7791
Epoch 1/1... Batch 420... Discriminator Loss: 0.6958... Generator Loss: 1.9020
Epoch 1/1... Batch 430... Discriminator Loss: 1.3681... Generator Loss: 0.5793
Epoch 1/1... Batch 440... Discriminator Loss: 0.6048... Generator Loss: 2.9248
Epoch 1/1... Batch 450... Discriminator Loss: 0.6125... Generator Loss: 2.4817
Epoch 1/1... Batch 460... Discriminator Loss: 0.6957... Generator Loss: 1.5059
Epoch 1/1... Batch 470... Discriminator Loss: 0.8473... Generator Loss: 1.3461
Epoch 1/1... Batch 480... Discriminator Loss: 0.6487... Generator Loss: 2.6059
Epoch 1/1... Batch 490... Discriminator Loss: 0.9997... Generator Loss: 3.4397
Epoch 1/1... Batch 500... Discriminator Loss: 0.7450... Generator Loss: 1.4979
Epoch 1/1... Batch 510... Discriminator Loss: 0.7253... Generator Loss: 2.7460
Epoch 1/1... Batch 520... Discriminator Loss: 0.4651... Generator Loss: 2.7675
Epoch 1/1... Batch 530... Discriminator Loss: 0.6232... Generator Loss: 1.7611
Epoch 1/1... Batch 540... Discriminator Loss: 0.7001... Generator Loss: 1.7315
Epoch 1/1... Batch 550... Discriminator Loss: 0.9662... Generator Loss: 1.6501
Epoch 1/1... Batch 560... Discriminator Loss: 0.5051... Generator Loss: 2.5372
Epoch 1/1... Batch 570... Discriminator Loss: 0.8150... Generator Loss: 1.5047
Epoch 1/1... Batch 580... Discriminator Loss: 0.6465... Generator Loss: 1.7365
Epoch 1/1... Batch 590... Discriminator Loss: 0.8000... Generator Loss: 2.7266
Epoch 1/1... Batch 600... Discriminator Loss: 0.5775... Generator Loss: 2.5845
Epoch 1/1... Batch 610... Discriminator Loss: 0.5796... Generator Loss: 2.8426
Epoch 1/1... Batch 620... Discriminator Loss: 0.5514... Generator Loss: 2.5244
Epoch 1/1... Batch 630... Discriminator Loss: 0.6328... Generator Loss: 1.8683
Epoch 1/1... Batch 640... Discriminator Loss: 0.6483... Generator Loss: 2.1176
Epoch 1/1... Batch 650... Discriminator Loss: 0.7445... Generator Loss: 2.0867
Epoch 1/1... Batch 660... Discriminator Loss: 0.9201... Generator Loss: 1.4898
Epoch 1/1... Batch 670... Discriminator Loss: 0.6601... Generator Loss: 2.6989
Epoch 1/1... Batch 680... Discriminator Loss: 0.6206... Generator Loss: 5.7432
Epoch 1/1... Batch 690... Discriminator Loss: 0.6621... Generator Loss: 1.7880
Epoch 1/1... Batch 700... Discriminator Loss: 0.5407... Generator Loss: 2.6516
Epoch 1/1... Batch 710... Discriminator Loss: 0.9217... Generator Loss: 1.2332
Epoch 1/1... Batch 720... Discriminator Loss: 0.6555... Generator Loss: 1.9888
Epoch 1/1... Batch 730... Discriminator Loss: 0.8124... Generator Loss: 1.6900
Epoch 1/1... Batch 740... Discriminator Loss: 0.7146... Generator Loss: 1.6573
Epoch 1/1... Batch 750... Discriminator Loss: 0.7137... Generator Loss: 1.9078
Epoch 1/1... Batch 760... Discriminator Loss: 1.1399... Generator Loss: 0.7781
Epoch 1/1... Batch 770... Discriminator Loss: 0.7995... Generator Loss: 1.4277
Epoch 1/1... Batch 780... Discriminator Loss: 0.5843... Generator Loss: 2.8795
Epoch 1/1... Batch 790... Discriminator Loss: 1.0014... Generator Loss: 1.8934
Epoch 1/1... Batch 800... Discriminator Loss: 0.7277... Generator Loss: 1.5580
Epoch 1/1... Batch 810... Discriminator Loss: 0.8284... Generator Loss: 1.4933
Epoch 1/1... Batch 820... Discriminator Loss: 1.4626... Generator Loss: 0.6312
Epoch 1/1... Batch 830... Discriminator Loss: 0.9807... Generator Loss: 1.2565
Epoch 1/1... Batch 840... Discriminator Loss: 0.6602... Generator Loss: 2.5491
Epoch 1/1... Batch 850... Discriminator Loss: 0.9031... Generator Loss: 1.5217
Epoch 1/1... Batch 860... Discriminator Loss: 0.6826... Generator Loss: 1.9352
Epoch 1/1... Batch 870... Discriminator Loss: 0.6180... Generator Loss: 2.1634
Epoch 1/1... Batch 880... Discriminator Loss: 0.9077... Generator Loss: 1.0753
Epoch 1/1... Batch 890... Discriminator Loss: 0.9521... Generator Loss: 1.0359
Epoch 1/1... Batch 900... Discriminator Loss: 0.6293... Generator Loss: 2.6400
Epoch 1/1... Batch 910... Discriminator Loss: 0.6975... Generator Loss: 1.8191
Epoch 1/1... Batch 920... Discriminator Loss: 1.0272... Generator Loss: 1.1181
Epoch 1/1... Batch 930... Discriminator Loss: 0.8169... Generator Loss: 3.4105
Epoch 1/1... Batch 940... Discriminator Loss: 1.0299... Generator Loss: 3.1979
Epoch 1/1... Batch 950... Discriminator Loss: 0.7062... Generator Loss: 2.2874
Epoch 1/1... Batch 960... Discriminator Loss: 0.9481... Generator Loss: 1.3202
Epoch 1/1... Batch 970... Discriminator Loss: 0.7403... Generator Loss: 2.2407
Epoch 1/1... Batch 980... Discriminator Loss: 1.0301... Generator Loss: 1.3169
Epoch 1/1... Batch 990... Discriminator Loss: 0.8350... Generator Loss: 2.1146
Epoch 1/1... Batch 1000... Discriminator Loss: 0.9284... Generator Loss: 1.7711
Epoch 1/1... Batch 1010... Discriminator Loss: 1.3899... Generator Loss: 4.8350
Epoch 1/1... Batch 1020... Discriminator Loss: 1.0234... Generator Loss: 1.4691
Epoch 1/1... Batch 1030... Discriminator Loss: 0.6590... Generator Loss: 2.1338
Epoch 1/1... Batch 1040... Discriminator Loss: 0.6334... Generator Loss: 2.2339
Epoch 1/1... Batch 1050... Discriminator Loss: 0.7896... Generator Loss: 2.9979
Epoch 1/1... Batch 1060... Discriminator Loss: 0.6952... Generator Loss: 2.0121
Epoch 1/1... Batch 1070... Discriminator Loss: 0.5991... Generator Loss: 2.2526
Epoch 1/1... Batch 1080... Discriminator Loss: 0.6074... Generator Loss: 2.7584
Epoch 1/1... Batch 1090... Discriminator Loss: 2.1897... Generator Loss: 0.2847
Epoch 1/1... Batch 1100... Discriminator Loss: 1.0680... Generator Loss: 1.0528
Epoch 1/1... Batch 1110... Discriminator Loss: 0.8565... Generator Loss: 1.4748
Epoch 1/1... Batch 1120... Discriminator Loss: 0.6835... Generator Loss: 2.6259
Epoch 1/1... Batch 1130... Discriminator Loss: 0.7589... Generator Loss: 1.7055
Epoch 1/1... Batch 1140... Discriminator Loss: 1.2822... Generator Loss: 0.7662
Epoch 1/1... Batch 1150... Discriminator Loss: 1.3073... Generator Loss: 0.7216
Epoch 1/1... Batch 1160... Discriminator Loss: 0.8307... Generator Loss: 1.8292
Epoch 1/1... Batch 1170... Discriminator Loss: 1.7124... Generator Loss: 0.5974
Epoch 1/1... Batch 1180... Discriminator Loss: 0.6531... Generator Loss: 2.3298
Epoch 1/1... Batch 1190... Discriminator Loss: 0.7696... Generator Loss: 1.9905
Epoch 1/1... Batch 1200... Discriminator Loss: 0.7351... Generator Loss: 2.2027
Epoch 1/1... Batch 1210... Discriminator Loss: 0.8114... Generator Loss: 1.7751
Epoch 1/1... Batch 1220... Discriminator Loss: 0.6558... Generator Loss: 4.6369
Epoch 1/1... Batch 1230... Discriminator Loss: 0.6003... Generator Loss: 2.4176
Epoch 1/1... Batch 1240... Discriminator Loss: 0.6093... Generator Loss: 2.0698
Epoch 1/1... Batch 1250... Discriminator Loss: 1.2293... Generator Loss: 0.7631
Epoch 1/1... Batch 1260... Discriminator Loss: 1.0764... Generator Loss: 1.2489
Epoch 1/1... Batch 1270... Discriminator Loss: 0.8211... Generator Loss: 2.7939
Epoch 1/1... Batch 1280... Discriminator Loss: 0.7923... Generator Loss: 1.4489
Epoch 1/1... Batch 1290... Discriminator Loss: 0.6443... Generator Loss: 2.0602
Epoch 1/1... Batch 1300... Discriminator Loss: 0.8929... Generator Loss: 1.3065
Epoch 1/1... Batch 1310... Discriminator Loss: 0.9454... Generator Loss: 1.3103
Epoch 1/1... Batch 1320... Discriminator Loss: 1.8360... Generator Loss: 0.3511
Epoch 1/1... Batch 1330... Discriminator Loss: 0.7802... Generator Loss: 1.7417
Epoch 1/1... Batch 1340... Discriminator Loss: 0.7765... Generator Loss: 1.4231
Epoch 1/1... Batch 1350... Discriminator Loss: 0.6215... Generator Loss: 3.0075
Epoch 1/1... Batch 1360... Discriminator Loss: 0.7021... Generator Loss: 2.0159
Epoch 1/1... Batch 1370... Discriminator Loss: 0.8692... Generator Loss: 1.2087
Epoch 1/1... Batch 1380... Discriminator Loss: 1.5694... Generator Loss: 4.8062
Epoch 1/1... Batch 1390... Discriminator Loss: 1.0889... Generator Loss: 1.2658
Epoch 1/1... Batch 1400... Discriminator Loss: 0.8122... Generator Loss: 1.5227
Epoch 1/1... Batch 1410... Discriminator Loss: 1.1949... Generator Loss: 0.7775
Epoch 1/1... Batch 1420... Discriminator Loss: 0.9593... Generator Loss: 1.4719
Epoch 1/1... Batch 1430... Discriminator Loss: 0.7496... Generator Loss: 2.1053
Epoch 1/1... Batch 1440... Discriminator Loss: 0.6977... Generator Loss: 1.8836
Epoch 1/1... Batch 1450... Discriminator Loss: 0.7123... Generator Loss: 1.6973
Epoch 1/1... Batch 1460... Discriminator Loss: 0.8697... Generator Loss: 1.0769
Epoch 1/1... Batch 1470... Discriminator Loss: 0.7603... Generator Loss: 2.7632
Epoch 1/1... Batch 1480... Discriminator Loss: 0.7518... Generator Loss: 1.7135
Epoch 1/1... Batch 1490... Discriminator Loss: 0.8872... Generator Loss: 1.4210
Epoch 1/1... Batch 1500... Discriminator Loss: 1.2590... Generator Loss: 0.7087
Epoch 1/1... Batch 1510... Discriminator Loss: 0.8636... Generator Loss: 1.1373
Epoch 1/1... Batch 1520... Discriminator Loss: 0.7312... Generator Loss: 1.4224
Epoch 1/1... Batch 1530... Discriminator Loss: 0.9014... Generator Loss: 1.1348
Epoch 1/1... Batch 1540... Discriminator Loss: 0.8702... Generator Loss: 2.6694
Epoch 1/1... Batch 1550... Discriminator Loss: 0.8481... Generator Loss: 1.3838
Epoch 1/1... Batch 1560... Discriminator Loss: 0.8146... Generator Loss: 2.4693
Epoch 1/1... Batch 1570... Discriminator Loss: 0.7166... Generator Loss: 2.3493
Epoch 1/1... Batch 1580... Discriminator Loss: 0.9279... Generator Loss: 1.2117
Epoch 1/1... Batch 1590... Discriminator Loss: 0.6027... Generator Loss: 3.1627
Epoch 1/1... Batch 1600... Discriminator Loss: 0.8995... Generator Loss: 1.0846
Epoch 1/1... Batch 1610... Discriminator Loss: 0.6167... Generator Loss: 2.1377
Epoch 1/1... Batch 1620... Discriminator Loss: 1.0956... Generator Loss: 0.9117
Epoch 1/1... Batch 1630... Discriminator Loss: 0.7848... Generator Loss: 2.1478
Epoch 1/1... Batch 1640... Discriminator Loss: 0.8153... Generator Loss: 1.4631
Epoch 1/1... Batch 1650... Discriminator Loss: 0.8298... Generator Loss: 1.5319
Epoch 1/1... Batch 1660... Discriminator Loss: 0.8092... Generator Loss: 1.4378
Epoch 1/1... Batch 1670... Discriminator Loss: 0.7607... Generator Loss: 1.5295
Epoch 1/1... Batch 1680... Discriminator Loss: 0.5876... Generator Loss: 3.1198
Epoch 1/1... Batch 1690... Discriminator Loss: 1.9680... Generator Loss: 4.4714
Epoch 1/1... Batch 1700... Discriminator Loss: 0.9601... Generator Loss: 1.2440
Epoch 1/1... Batch 1710... Discriminator Loss: 0.8489... Generator Loss: 1.7197
Epoch 1/1... Batch 1720... Discriminator Loss: 0.7610... Generator Loss: 1.6927
Epoch 1/1... Batch 1730... Discriminator Loss: 0.7552... Generator Loss: 1.8308
Epoch 1/1... Batch 1740... Discriminator Loss: 0.6980... Generator Loss: 1.6306
Epoch 1/1... Batch 1750... Discriminator Loss: 1.1639... Generator Loss: 0.8175
Epoch 1/1... Batch 1760... Discriminator Loss: 0.7616... Generator Loss: 2.4181
Epoch 1/1... Batch 1770... Discriminator Loss: 0.5599... Generator Loss: 3.1525
Epoch 1/1... Batch 1780... Discriminator Loss: 0.5395... Generator Loss: 2.8335
Epoch 1/1... Batch 1790... Discriminator Loss: 1.2731... Generator Loss: 0.7004
Epoch 1/1... Batch 1800... Discriminator Loss: 0.7583... Generator Loss: 2.6500
Epoch 1/1... Batch 1810... Discriminator Loss: 0.6342... Generator Loss: 3.1745
Epoch 1/1... Batch 1820... Discriminator Loss: 0.7448... Generator Loss: 2.4757
Epoch 1/1... Batch 1830... Discriminator Loss: 1.1271... Generator Loss: 0.8675
Epoch 1/1... Batch 1840... Discriminator Loss: 0.7156... Generator Loss: 1.6916
Epoch 1/1... Batch 1850... Discriminator Loss: 0.6246... Generator Loss: 2.1409
Epoch 1/1... Batch 1860... Discriminator Loss: 0.7100... Generator Loss: 2.4391
Epoch 1/1... Batch 1870... Discriminator Loss: 0.7602... Generator Loss: 1.5511
Epoch 1/1... Batch 1880... Discriminator Loss: 1.1349... Generator Loss: 0.8251
Epoch 1/1... Batch 1890... Discriminator Loss: 0.8157... Generator Loss: 2.2761
Epoch 1/1... Batch 1900... Discriminator Loss: 0.6466... Generator Loss: 2.2700
Epoch 1/1... Batch 1910... Discriminator Loss: 0.9261... Generator Loss: 1.1635
Epoch 1/1... Batch 1920... Discriminator Loss: 0.9071... Generator Loss: 1.2466
Epoch 1/1... Batch 1930... Discriminator Loss: 0.7644... Generator Loss: 1.3492
Epoch 1/1... Batch 1940... Discriminator Loss: 0.7888... Generator Loss: 1.3729
Epoch 1/1... Batch 1950... Discriminator Loss: 2.0785... Generator Loss: 0.3061
Epoch 1/1... Batch 1960... Discriminator Loss: 1.7264... Generator Loss: 0.3387
Epoch 1/1... Batch 1970... Discriminator Loss: 0.7304... Generator Loss: 2.1674
Epoch 1/1... Batch 1980... Discriminator Loss: 0.6829... Generator Loss: 1.9923
Epoch 1/1... Batch 1990... Discriminator Loss: 0.8585... Generator Loss: 2.0379
Epoch 1/1... Batch 2000... Discriminator Loss: 0.6116... Generator Loss: 2.0300
Epoch 1/1... Batch 2010... Discriminator Loss: 0.6105... Generator Loss: 2.1823
Epoch 1/1... Batch 2020... Discriminator Loss: 0.9864... Generator Loss: 0.9709
Epoch 1/1... Batch 2030... Discriminator Loss: 0.8625... Generator Loss: 1.2417
Epoch 1/1... Batch 2040... Discriminator Loss: 0.8499... Generator Loss: 1.2808
Epoch 1/1... Batch 2050... Discriminator Loss: 0.7984... Generator Loss: 1.6229
Epoch 1/1... Batch 2060... Discriminator Loss: 0.6917... Generator Loss: 1.7419
Epoch 1/1... Batch 2070... Discriminator Loss: 0.7808... Generator Loss: 2.3985
Epoch 1/1... Batch 2080... Discriminator Loss: 0.6120... Generator Loss: 1.9714
Epoch 1/1... Batch 2090... Discriminator Loss: 0.5726... Generator Loss: 3.1907
Epoch 1/1... Batch 2100... Discriminator Loss: 0.5238... Generator Loss: 2.9318
Epoch 1/1... Batch 2110... Discriminator Loss: 0.8352... Generator Loss: 1.5104
Epoch 1/1... Batch 2120... Discriminator Loss: 0.8542... Generator Loss: 2.4069
Epoch 1/1... Batch 2130... Discriminator Loss: 1.5765... Generator Loss: 0.4923
Epoch 1/1... Batch 2140... Discriminator Loss: 1.4135... Generator Loss: 0.6058
Epoch 1/1... Batch 2150... Discriminator Loss: 0.6917... Generator Loss: 1.9994
Epoch 1/1... Batch 2160... Discriminator Loss: 0.8351... Generator Loss: 1.2812
Epoch 1/1... Batch 2170... Discriminator Loss: 0.8263... Generator Loss: 2.5703
Epoch 1/1... Batch 2180... Discriminator Loss: 0.9129... Generator Loss: 1.3003
Epoch 1/1... Batch 2190... Discriminator Loss: 1.8003... Generator Loss: 0.3085
Epoch 1/1... Batch 2200... Discriminator Loss: 0.8747... Generator Loss: 1.1275
Epoch 1/1... Batch 2210... Discriminator Loss: 0.9295... Generator Loss: 1.4875
Epoch 1/1... Batch 2220... Discriminator Loss: 1.2540... Generator Loss: 0.6702
Epoch 1/1... Batch 2230... Discriminator Loss: 0.9307... Generator Loss: 1.1813
Epoch 1/1... Batch 2240... Discriminator Loss: 0.9018... Generator Loss: 1.2160
Epoch 1/1... Batch 2250... Discriminator Loss: 1.1596... Generator Loss: 0.7519
Epoch 1/1... Batch 2260... Discriminator Loss: 0.8034... Generator Loss: 1.8588
Epoch 1/1... Batch 2270... Discriminator Loss: 0.8026... Generator Loss: 1.4918
Epoch 1/1... Batch 2280... Discriminator Loss: 0.6496... Generator Loss: 2.0061
Epoch 1/1... Batch 2290... Discriminator Loss: 1.3491... Generator Loss: 0.6645
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Epoch 1/1... Batch 5410... Discriminator Loss: 1.0863... Generator Loss: 0.9685
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Epoch 1/1... Batch 5500... Discriminator Loss: 0.9013... Generator Loss: 1.0857
Epoch 1/1... Batch 5510... Discriminator Loss: 0.9332... Generator Loss: 1.0104
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Epoch 1/1... Batch 5660... Discriminator Loss: 1.0176... Generator Loss: 0.9922
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Epoch 1/1... Batch 5680... Discriminator Loss: 0.9509... Generator Loss: 1.4734
Epoch 1/1... Batch 5690... Discriminator Loss: 1.4168... Generator Loss: 0.5161
Epoch 1/1... Batch 5700... Discriminator Loss: 0.7994... Generator Loss: 1.1903
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Epoch 1/1... Batch 5720... Discriminator Loss: 0.6868... Generator Loss: 1.8087
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Epoch 1/1... Batch 5790... Discriminator Loss: 0.9401... Generator Loss: 1.0024
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Epoch 1/1... Batch 5840... Discriminator Loss: 0.6821... Generator Loss: 2.1401
Epoch 1/1... Batch 5850... Discriminator Loss: 0.6971... Generator Loss: 2.0042
Epoch 1/1... Batch 5860... Discriminator Loss: 0.9975... Generator Loss: 1.8427
Epoch 1/1... Batch 5870... Discriminator Loss: 0.8164... Generator Loss: 2.0232
Epoch 1/1... Batch 5880... Discriminator Loss: 0.9129... Generator Loss: 1.5408
Epoch 1/1... Batch 5890... Discriminator Loss: 1.0734... Generator Loss: 0.9565
Epoch 1/1... Batch 5900... Discriminator Loss: 0.7952... Generator Loss: 1.6623
Epoch 1/1... Batch 5910... Discriminator Loss: 0.8177... Generator Loss: 1.2643
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Epoch 1/1... Batch 6060... Discriminator Loss: 0.9320... Generator Loss: 1.1522
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Epoch 1/1... Batch 6080... Discriminator Loss: 1.1325... Generator Loss: 1.8796
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Epoch 1/1... Batch 6100... Discriminator Loss: 0.8073... Generator Loss: 1.5607
Epoch 1/1... Batch 6110... Discriminator Loss: 0.8879... Generator Loss: 1.5226
Epoch 1/1... Batch 6120... Discriminator Loss: 1.0433... Generator Loss: 1.0157
Epoch 1/1... Batch 6130... Discriminator Loss: 0.9299... Generator Loss: 1.3351
Epoch 1/1... Batch 6140... Discriminator Loss: 1.0613... Generator Loss: 2.1097
Epoch 1/1... Batch 6150... Discriminator Loss: 0.7630... Generator Loss: 1.6857
Epoch 1/1... Batch 6160... Discriminator Loss: 0.7353... Generator Loss: 1.3994
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Epoch 1/1... Batch 6180... Discriminator Loss: 0.8215... Generator Loss: 1.1919
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Epoch 1/1... Batch 6200... Discriminator Loss: 0.9369... Generator Loss: 0.8451
Epoch 1/1... Batch 6210... Discriminator Loss: 0.8338... Generator Loss: 1.1446
Epoch 1/1... Batch 6220... Discriminator Loss: 0.8234... Generator Loss: 1.2544
Epoch 1/1... Batch 6230... Discriminator Loss: 0.7915... Generator Loss: 1.5477
Epoch 1/1... Batch 6240... Discriminator Loss: 0.6995... Generator Loss: 1.8365
Epoch 1/1... Batch 6250... Discriminator Loss: 0.7412... Generator Loss: 1.5114
Epoch 1/1... Batch 6260... Discriminator Loss: 0.8363... Generator Loss: 1.2006
Epoch 1/1... Batch 6270... Discriminator Loss: 0.9002... Generator Loss: 1.7056
Epoch 1/1... Batch 6280... Discriminator Loss: 0.9328... Generator Loss: 1.4307
Epoch 1/1... Batch 6290... Discriminator Loss: 1.0371... Generator Loss: 1.0084
Epoch 1/1... Batch 6300... Discriminator Loss: 0.7784... Generator Loss: 1.8158
Epoch 1/1... Batch 6310... Discriminator Loss: 0.6802... Generator Loss: 1.9574
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Epoch 1/1... Batch 6330... Discriminator Loss: 1.2572... Generator Loss: 0.8458

提交项目

提交本项目前,确保运行所有 cells 后保存该文件。

保存该文件为 "dlnd_face_generation.ipynb", 并另存为 HTML 格式 "File" -> "Download as"。提交项目时请附带 "helper.py" 和 "problem_unittests.py" 文件。